Class 2

Importing, Merging, and Restructuring Data

Prepare

Before starting this class:

 

📦 Install readr, dplyr, tidyr, and ggplot2 packages

⬇️ Download sample data files: (right-click to download linked file)

Outline

 

  • Import: readr

    • File paths

    • read_csv() and read_delim()

  • Merge: dplyr

    • bind_() functions

    • join_() functions

  • Restructure: tidyr

    • pivot_wider()

    • pivot_longer()

Pipe Operator

Pipe Operator

 

The pipe operator allows you to chain together a set of functions to conduct a sequence of manipulations on it. Conceptually, here’s what code written using the pipe operator looks like:

 

data |>
  step1 |>
  step2 |>
  step3


We start with our data. Then we do step1. Then we do step2. Then we do step3. The pipe ties it all together, enabling us to do multiple things to our data, all in one execution of code.

Pipe Operator

 

There are different approaches to writing code that performs multiple functions on the same object.

 

Here is the standard, non-pipe operator way:

# three steps: filter, calculate a mean, then select only some columns to keep
data_new <- filter(data, y != "c")
data_new <- mutate(data_new, x_mean = mean(x))
data_new <- select(data_new, y, x_mean)


An alternative is to use the pipe operator |>

# three steps: filter, calculate a mean, then select only some columns to keep
data_new <- data |>
  filter(y != "c") |>
  mutate(x_mean = mean(x)) |>
  select(y, x_mean)

Pipe Operator

 

# three steps: filter, calculate a mean, then select only some columns to keep
data_new <- data |>
  filter(y != "c") |>
  mutate(x_mean = mean(x)) |>
  select(y, x_mean)


With the pipe operator, the result of the previous line gets passed (or piped) onto the next function.

  • The first line in this example is simply specifying the data frame that is being passed from one line to the next.

  • Notice how I did not have to specify data inside the filter(), mutate(), and select(), functions. This makes the code more concise and easier to read.

  • The end result of the last function, then gets assigned to data_new <-.

Import

File Paths

R needs to know the full file path to the file on your computer in order to import it

 

  • Absolute file paths

On Macs:

Users/username/projects/project_name/a_file.csv

 

On Windows:

C:\username\projects\project_name\a_file.csv

Three approaches to getting the right file path

 

  1. Use setwd()
  2. Use the RStudio Import Dataset GUI
  3. Use RProjects and here()

  • Never… ever… ever… use option 1, setwd().

  • Instead, you should use RProjects and here(). But we will not cover this until Class 5.

  • For now, we can just use the RStudio Import Dataset GUI to find the absolute file path.

RStudio Import Dataset GUI

 

RStudio Import Dataset GUI

 

RStudio Import Dataset GUI

 

 

  • Select Browse on the top right and select the data file you want to import.

  • The Data Preview window will let you see if it is importing it in the right format.

  • You can change the Import Options below.

  • Click on the 📋 icon above the Code Preview window to copy the code.

  • Click on Cancel to exit out of the Import GUI window

  • Paste the code into your Untitled.R script

    • The most useful thing here will be the absolute file path.

 

 

RStudio Import Dataset GUI

 

 

 

 

Exploring Your Data

When you want to just explore some data and don’t care about creating a reproducible script it can be perfectly acceptable to not copy and paste the code from Code Preview window and just select the Import button.

 

 

Types of Data Files

 

  • csv

  • tab-delimited

csv Data Files

 

  • CSV stands for “Comma-Separated Values.”

  • csv files are typically saved with .csv file extension (e.g., datafile.csv)

  • csv files are by far the easiest files to import into R and most software programs.

  • I suggest any time you want to save/output a data file to your computer, do it in csv format.

tab-delimited Data Files

 

  • tab-delimited files are saved with the more standard .txt file extension (e.g., datafile.txt)

  • There are a lot of different types of delimiters

  • tab-delimited files are a little more tedious to import

  • You have to memorize more arguments to import tab-delimited files

Importing Data Files

readr

 

library(readr)

# csv
data_import <- read_csv("filepath/datafile.csv")

# tab-delimited
data_import <- read_delim("filepath/datafile.txt", delim = "\t", 
                          escape_double = FALSE, trim_ws = TRUE)


  • Use the RStudio Import Dataset GUI to get the filepath that you will need

  • In Class 5 you will learn a better way to specify file paths using RProjects and here::here()

Merge

Merging Data Frames

 

You might find yourself in a situation where you need to import multiple data files and merge them into a single data frame. There are two general classes of merging data

 

  • Bind

  • Join

Bind

Combining data frames together by stacking either the rows or columns

 

Row Bind: same columns but different rows and stack them on top of each other

Bind

Combining data frames together by stacking either the rows or columns

 

Column Bind: same rows but different columns and stacks them side-by-side. This is a much less common situation than a row bind and can usually be accomplished with a join instead

Bind

dplyr

 

library(dplyr)

# row bind
data_merged <- bind_rows(data_1, data_2)

# column bind
data_merged <- bind_cols(data_1, data_2)

Join

Merging data frames together that have at least one column in common with a mix of shared and unique entries in that column (e.g. Subject IDs).

 

For a full list and detailed description of the _join() functions see the dplyr documentation

Full Join

For the most part you can get away with just knowing how to do a full join using full_join() from the dplyr package

 

data_merged <- full_join(data_1, data_2, by = "Subject")


  • You need to specify what are the key column(s) to join by - columns that are common between the data frames.

  • Often times there is more than one key column that the data frames need to be joined by:

data_merged <- full_join(data_1, data_2, by = c("Subject", "Session"))

Restructure

Restructure

 

The exact same data can be structured in different ways

There are two main formats that any data set can be structured as:

 

Wide: Variables are spread out across columns, making the data frame wider

 

ID Stress Creativity Memory
1 5 7 8
2 3 6 7
3 4 8 6

Long: Variables and values are spread across rows, making the data frame longer

 

ID Test Type Score
1 Stress 5
1 Creativity 7
1 Memory 8
2 Stress 3
2 Creativity 6
2 Memory 7
3 Stress 4
3 Creativity 8
3 Memory 6

 

Restructure

 

And actually, you can have a mix of wide and long formatted data in a single data frame.

 

Participant ID Session Stress Level Creativity Memory
1 1 5 7 8
1 2 4 8 9
1 3 3 9 10
2 1 6 6 7
2 2 5 7 8
2 3 4 8 9
3 1 4 8 6
3 2 3 9 7
3 3 2 10 8

Restructure

 

  • A good rule of thumb for formatting data is to have your variables (IVs and DVs) each have their own column.

  • This often results in:

    • Measured variables in wide format

    • Experimental conditions or repeated-measures in long format

  • Restructuring data involves changing the structure from long-to-wide (wider) or wide-to-long (longer)

  • The tidyr package provides useful functions to do this:

    • pivot_wider() “widens” data from long-to-wide

    • pivot_longer() “lengthens” data from wide-to-long

pivot_wider()

 

  • Using the example data sets on previous slides, let’s restructure the long data frame to a wide format

  • First let’s create the data frame - you can just copy and paste this code in an Untitled.R script:

 

data_long <- data.frame(
  ParticipantID = rep(1:3, each = 3),
  TestType = rep(c("Stress Level", "Creativity", "Memory"), times = 3),
  Score = c(5, 7, 8, 3, 6, 7, 4, 8, 6)
  )

View(data_long)

pivot_wider()

 

The two main arguments to specify in pivot_wider() are

  • names_from: The column name that contains the variables to create new columns by (e.g. “Test Type”). The values in this column will become column names in the wider data format.

  • values_from: The column name that contains the values (e.g. “Score”).

Now we can use pivot_wider() to convert it to a wide format:

 

library(tidyr)

data_wide <- data_long |>
  pivot_wider(names_from = TestType,
              values_from = Score)

View(data_wide)

 

 

 

Column Names

R does not like column names to have spaces in them, but it does allow it.

Notice how in data_wide the column Stress Level contains a space. This is because the value in data_long had a space, which was not a problem then.

clean_names() from janitor provides a convenient way to get rid of spaces and replace them with an _

library(janitor)

data_wide_clean <- clean_names(data_wide, case = "parse")

pivot_longer()

 

The three main arguments to specify in pivot_longer() are:

  • cols: The column names that will be restructured to a longer format

  • names_to: The new column name that will contain values which correspond to the column names in the wide data

  • values_to: The new column name that will contain the actual values in the wide data

Using pivot_longer() we can restructure the data back to long format:

 

data_long_again <- data_wide |>
  pivot_longer(cols = any_of(c("Stress Level", "Creativity", "Memory")),
               names_to = "TestType",
               values_to = "Score")

View(data_long_again)

Mixed Wide and Long

 

Converting from a wide data format to a mix of wide and long can be more complicated

Let’s say we have a wide data set with multiple sessions of Stress , Creativity, and Memory.

 

Copy and paste this:

data_sessions_wide <- tibble(
  ParticipantID = 1:3,
  StressLevel_S1 = c(5, 6, 4),
  Creativity_S1 = c(7, 6, 8),
  Memory_S1 = c(8, 7, 6),
  StressLevel_S2 = c(4, 5, 3),
  Creativity_S2 = c(8, 7, 9),
  Memory_S2 = c(9, 8, 7),
  StressLevel_S3 = c(3, 4, 2),
  Creativity_S3 = c(9, 8, 10),
  Memory_S3 = c(10, 9, 8)
)

View(data_sessions_wide)

Mixed Wide and Long

 

Let’s restructure this to 4 columns: Session, Stress Level, Creativity, and Memory.

There are two strategies for doing this:

  1. Using pivot_longer(), separate(), and then pivot_wider()
  2. Use more complicated syntax in pivot_longer()
# 1. Using pivot_longer(), separate(), and then pivot_wider()
data_sessions_1 <- data_sessions_wide |>
  pivot_longer(cols = contains("_S"),
               names_to = "Session",
               values_to = "Score") |>
  separate(Session, into = c("Test", "Session")) |>
  pivot_wider(names_from = Test,
              values_from = Score)

# 2. Use more complicated syntax in pivot_longer()
data_sessions_2 <- data_sessions_wide |>
  pivot_longer(cols = contains("_S"),
               names_to = c(".value", "Session"),
               names_pattern = "(.*)_(S\\d)")

Tidy Select Functions

 

Note

You may find yourself in a situation where you need to select multiple columns in a argument but it can be tedious to type each every column, especially if the number of columns is larger.

There are what is known as tidy select functions to easily select column names, particularly if there is a consistent pattern to those column names.

This can be very useful in functions like pivot_longer(). In the examples provided on previous slides, we used some tidy select functions: any_of() and contains().

To learn more about what tidy select functions are available and what they do read the tidy select documentation.

Class Activity

Class Activity

Note that this activity was created with the assistance of ChatGPT 4.

 

Let’s consider a study investigating the impact of sleep quality interventions on cognitive performance and mood across several weeks. In this scenario, participants are exposed to two interventions (Sleep Hygiene Education and Relaxation Techniques) and their cognitive performance scores and mood ratings are recorded weekly.

 

You’re analyzing data from a psychology experiment where participants’ cognitive performance scores and mood ratings are recorded across four weeks for two interventions. The data is split into two files, one with the cognitive performance data (class_2_cog_data.csv) and another with the mood ratings data (class_2_mood_data.txt), but the same participants across each data set.

 

Your task will be to import, restructure, and merge the data so that we can graphically visualize the results of the experiment.

 

Reference these slides and/or the class lecture materials to complete the activity.

Setup

 

  1. Create a new R script and save it as class_2_activity.R

  2. Load the following packages at the top of your script

    • readr, dplyr, tidyr, ggplot2

Import

 

  1. Import the data files

  2. Get to know the data (use what you learned from Class 1)

    • What are the column names?

    • What type of values are in the columns?

    • How many participants are in the study?

    • How many are in each of the intervention conditions?

    • Approximately, what are the range of values on cognitive performance and mood ratings?

      • Hint: You can click on the columns names when viewing the data frame to toggle between sorting from low-to-high and high-to-low

    • Are there are any missing values in the data?

Restructure

 

  1. Restructure each data set to long format with week spread across rows instead of columns and one column for cognitive score or mood rating

The data frames should look something like

 

participant_id intervention_type week cognitive_score
1 Sleep Hygiene 1 76.09
1 Sleep Hygiene 2 75.34
1 Sleep Hygiene 3 75.09
1 Sleep Hygiene 4 78.85
2 Sleep Hygiene 1 72.30

Restructure

 

After you have restructured the data frames, you will notice that the values in the week column also contain the string: cognitive_week_ or mood_week_

This is redundant information with the column names and can be removed to clean up the format of the data. The following line of code can be used to to do so.

 

mutate(week = parse_number(week))

# e.g., you can pipe the result of pivot_longer() to this mutate() code

data_long <- data |>
  pivot_longer() |>
  mutate(week = parse_number(week))

Merge

 

  1. Merge the two long data frames into one data frame.

    • Hint: There is more than one key column that the data frames need to be joined by.

Plot

 

Excellent! You have gotten the data ready to visualize. Graphical visualization is a great way to find patterns in your data. Let’s take a look at the result of this experiment! (note that this data is only simulated).

 

You are going to create plots that look something like this but I will show you how to further customize the design of the plot

Plot

 

  1. Plot the cognitive performance data:

    Create a plot showing the cognitive scores (y-axis) across weeks (x-axis) by intervention type (color). See previous slide

     

    1. First add the data and aesthetic layers - how the data map onto the plot scales and axes

       

      # change the following values to fit your data
# data, var_x, var_y, var_color

ggplot(data, aes(x = var_x, y = var_y,
                 color = var_color, group = var_color))

This will not plot the data yet, just setup the y and x-axis of the plot. Move on to the next slide to add the actual data.

7. Plot the Cognitive Performance Data

 

  1. Now let’s add in the geometries layer - the visual elements used for the data
ggplot(data, aes(x = var_x, y = var_y,
                 color = var_color, group = var_color))
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = .9) +
  stat_summary(fun = mean, geom = "line", size = .5) +
  stat_summary(fun = mean, geom = "point", size = 1.5)


  • No need to modify the code here.

  • For now don’t worry about what these are doing, we will cover graphical visualization in more detail in later chapters.

  • But you can see that we are plotting three geom layers, errorbar, line, and point. We need to use stat_summary() because the data are not aggregated, this function will aggregate the data across participants using fun = mean.

7. Plot the Cognitive Performance Data

 

  1. Finally, let’s make this plot a little prettier
# change the following values based on your preference
# the width, and size values in stat_summary()
# lower limit and upper limit in coord_cartesian()
# the title, x, y, and color in labs()
# choose a ggplot2 theme

ggplot(data, aes(x = var_x, y = var_y,
                 color = var_color, group = var_color)) +
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = .9) +
  stat_summary(fun = mean, geom = "line", size = .5) +
  stat_summary(fun = mean, geom = "point", size = 1.5) +
  coord_cartesian(ylim = c(72, 80)) +
  labs(title = "Give the plot a title", 
       x = "change x-axis label", 
       y = "change y-axis label", 
       color = "change legend title") +
  scale_color_brewer(palette = "Set1") +
  theme_() # choose a ggplot2 theme

See next slide for more details

Make the plot look nicer

 

Change the following values based on your preference

  • the width of the error bars: stat_summary(width = .9)
  • the size of the lines: stat_summary(size = .5)
  • the size of the data points: stat_summary(size = .1.5)
  • the lower and upper limit of the y-axis (zoom in or out): coord_cartesian(ylim = c(lower_limit, upper_limit)
  • add a plot title
  • the y-axis, x-axis, and legend labels. e.g., no underscores, capitalize words, etc.
  • choose a brewer color palette: scale_color_brewer(palette = "Set1")
  • choose a ggplot2 theme:
    • theme_bw() , theme_light() , theme_dark() , theme_minimal() , theme_classic() , theme_void()

Plot

 

  1. Plot the mood data:

    • Copy and paste the code you wrote for plotting the cognitive performance data.

    • Change values as needed

      • The variable to be plotted on the y-axis aes(y = )

      • the lower and upper limits of the y-axis coord_cartesian(ylim = c(lower_limit, upper_limit)

      • plot title, y-axis, x-axis, and legend labels

Plot

 

  1. Save the plots as a file on your computer

    • Place the following code directly below the code for creating each of the plots.

    • It will save the last generated plot to a file.

    • Change the file name for each plot.

ggsave(here("folder/path/class_2_cognitive_plot.png"), 
       width = 6, height = 4, dpi = 300)

Plot

 

  1. Organize and clean up your script

    • Load all packages at the top of your script
    • Get rid of code that is not a necessary step for creating the plots
    • Add comment headers to sections of your code: e.g., # load packages

Reproducible Script

You should attempt to complete the activity without looking at this code

Code 
# load packages
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# import data
cog_import <- read_csv("data/class_2_cog_data.csv")
mood_import <- read_delim("data/class_2_mood_data.txt", 
                          delim = "\t", escape_double = FALSE, trim_ws = TRUE)

# restructure data
cog_data <- cog_import |>
  pivot_longer(cols = starts_with("cognitive_week"), 
               names_to = "week", 
               values_to = "cognitive_score") |>
  mutate(week = parse_number(week))

mood_data <- mood_import |>
  pivot_longer(cols = starts_with("mood_week"), 
               names_to = "week", 
               values_to = "mood_score") |>
  mutate(week = parse_number(week))

# merge data
data_merged <- full_join(cog_data, mood_data, 
                         by = c("participant_id", "intervention_type", "week"))

# plot data
ggplot(data_merged, aes(x = week, y = cognitive_score, 
                        color = intervention_type, group = intervention_type)) +
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = .25) +
  stat_summary(fun = mean, geom = "line", size = 1) +
  stat_summary(fun = mean, geom = "point", size = 2) +
  coord_cartesian(ylim = c(65, 85)) +
  labs(title = "Cognitive Scores Across Weeks by Intervention Type", 
       x = "Week", 
       y = "Cognitive Score", 
       color = "Intervention Type") +
  scale_color_brewer(palette = "Set1") +
  theme_classic()

ggsave("images/class_2_cognitive_plot.png", 
       width = 6, height = 4, dpi = 300)

ggplot(data_merged, aes(x = week, y = mood_score, 
                        color = intervention_type, group = intervention_type)) +
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = .25) +
  stat_summary(fun = mean, geom = "line", size = 1) +
  stat_summary(fun = mean, geom = "point", size = 2) +
  coord_cartesian(ylim = c(60, 80)) +
  labs(title = "Mood Across Weeks by Intervention Type", 
       x = "Week", 
       y = "Mood", 
       color = "Intervention Type") +
  scale_color_brewer(palette = "Set1") +
  theme_classic()

ggsave("images/class_2_mood_plot.png", 
       width = 6, height = 4, dpi = 300)